Does patient-based data monitoring allow us to reconsider the relationship between IQC and EQA?
-
Tony Badrick
,
John Sioufi
Abstract
Currently, there is no accepted standard practice for determining the frequency of EQA challenge. The challenge frequency has evolved based on history and local requirements. However, EQA frequency should be based on identifying patient risks caused by poorly performing laboratories, methods, or processes in any phase of the total testing cycle. The role of IQC is to ensure result consistency from day to day and to halt reporting of results if there is an analytical failure. Historically, both activities have been based on synthetic control material. With the development of patient-based approaches to IQC and EQA, it is possible to continuously monitor analytical systems using the same patient parameter, usually the mean or median. These techniques can provide laboratories with additional information to reduce patient risk. There are limitations of Patient-Based Quality Assurance (PBQA), fundamentally the lack of middleware and connection of the analyzers to the EQA provider. It cannot be used to monitor the success of harmonization/standardization of assays to a reference measurement procedure. But the use of patient-based approaches offers an opportunity to reconsider how EQA can be undertaken and the relationship between IQC and EQA. If PBRTQC and PBQA could be implemented to provide daily peer group comparisons, then method-specific bias could be identified quickly by a laboratory. If this could be supplemented with a commutable, reference value assigned EQA program, then monitoring harmonization/standardization of assays to a reference measurement procedure could be achieved.
Funding source: None
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Hammerling, JA. A review of medical errors in laboratory diagnostics and where we are today. Lab Med 2012;43:41–4. https://doi.org/10.1309/lm6er9wjr1ihqauy.Search in Google Scholar
2. Sciacovelli, L, Secchiero, S, Zardo, L, Plebani, M. The role of the external quality assessment. Biochem Med (Zagreb) 2010;20:160–4. https://doi.org/10.11613/bm.2010.019.Search in Google Scholar
3. Badrick, T. Quality leadership and quality control. Clin Biochem Rev 2003;24:81–93.Search in Google Scholar
4. Jones, GRD, Delatour, V, Badrick, T. Metrological traceability and clinical traceability of laboratory results – the role of commutability in external quality assurance. Clin Chem Lab Med 2022;60:669–74. https://doi.org/10.1515/cclm-2022-0038.Search in Google Scholar PubMed
5. Parvin, C. Assessing the impact of the frequency of quality control testing on the quality of reported patient results. Clin Chem 2008;54:2049–54. https://doi.org/10.1373/clinchem.2008.113639.Search in Google Scholar PubMed
6. Westgard, JO, Bayat, H, Westgard, SA. Planning SQC strategies and adapting QC frequency for patient risk. Clin Chim Acta 2021;523:1–5. https://doi.org/10.1016/j.cca.2021.08.028.Search in Google Scholar PubMed
7. Thomas, A. External quality assessment in laboratory medicine: is there a rationale to determine frequency of surveys? Accred Qual Assur 2009;14:439–44. https://doi.org/10.1007/s00769-009-0563-2.Search in Google Scholar
8. Libeer, JC, Baadenhaijsen, H, Fraser, CG, Petersen, H, Ricos, C, Stockl, D, et al.. Classification of external quality assessment schemes (EQA) according to objectives of method and participant bias and standard deviation. Eur J Clin Chem Clin Biochem 1996;34:665–78.Search in Google Scholar
9. Badrick, T, Jones, G, Miller, WG, Panteghini, M, Quintenz, A, Sandberg, S, et al.. Differences between educational and regulatory external quality assurance/proficiency testing schemes. Clin Chem 2022;7:1–7. https://doi.org/10.1093/clinchem/hvac132.Search in Google Scholar PubMed
10. Badrick, T, Miller, WG, Panteghini, M, Delatour, V, Berghall, H, MacKenzie, F, et al.. Interpreting EQA - understanding why commutability of materials matters. Clin Chem 2022;68:494–500. https://doi.org/10.1093/clinchem/hvac002.Search in Google Scholar PubMed
11. Miller, WG, Jones, GRD, Horowitz, GL, Weykamp, C. Proficiency testing/external quality assessment: current challenges and future directions. Clin Chem 2011;57:1670–80. https://doi.org/10.1373/clinchem.2011.168641.Search in Google Scholar PubMed
12. Greg, MW, Greenberg, N, Budd, J, Delatour, V, IFCC Working Group on Commutability in Metrological Traceability. The evolving role of commutability in metrological traceability. Clin Chim Acta 2021;514:84–9. https://doi.org/10.1016/j.cca.2020.12.021.Search in Google Scholar PubMed
13. Miller, WG, Erek, A, Cunningham, TD, Oladipo, O, Scott, MG, Johnson, RE. Commutability limitations influence quality control results with different reagent lots. Clin Chem 2011;57:76–83. https://doi.org/10.1373/clinchem.2010.148106.Search in Google Scholar PubMed
14. Braga, F, Panteghini, M. Commutability of reference and control materials: an essential factor for assuring the quality of measurements in laboratory medicine. Clin Chem Lab Med 2019;57:967–73. https://doi.org/10.1515/cclm-2019-0154.Search in Google Scholar PubMed
15. Badrick, T, Cervinski, M, Loh, TP. A primer on patient-based quality control techniques. Clin Biochem 2019;64:1–5. https://doi.org/10.1016/j.clinbiochem.2018.12.004.Search in Google Scholar PubMed
16. Thienpont, LM, Stöckl, D. Percentiler and Flagger – low-cost, on-line monitoring of laboratory and manufacturer data and significant surplus to current external quality assessment. Lab Med 2018;42:289–96.10.1515/labmed-2018-0030Search in Google Scholar
17. Badrick, T, Loh, TP. Integrating patient-based quality control and patient-based quality assurance. Clin Biochem 2024;124. https://doi.org/10.1016/j.clinbiochem.2024.110708.Search in Google Scholar PubMed
18. Van Rossum, HH, Bietenbeck, A, Cervinski, MA, Katayev, A, Loh, TP, Badrick, TC. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice. Clin Chem Lab Med 2021;59:1213–20. https://doi.org/10.1515/cclm-2021-0072.Search in Google Scholar PubMed
19. Fleming, JK, Katayev, A. Changing the paradigm of laboratory quality control through implementation of real-time test results monitoring: for patients by patients. Clin Biochem 2015;48:508–13. https://doi.org/10.1016/j.clinbiochem.2014.12.016.Search in Google Scholar PubMed
20. Westgard, S. The 2017 great global QC survey results [Internet]; 2017. Available from: https://www.westgard.com/great-global-qc-survey-results.htm.Search in Google Scholar
21. Rosenbaum, MW, Flood, JG, Melanson, SEF, Baumann, NA, Marzinke, MA, Rai, AJ, et al.. Quality control practices for chemistry and immunochemistry in a cohort of 21 large academic medical centers. Am J Clin Pathol 2018;150:96–104. https://doi.org/10.1093/ajcp/aqy033.Search in Google Scholar PubMed
22. Giannoli, JM, Bernard, M, L’Hirondel, J, Heim, A, Badrick, T. A model for managing quality control for a network of clinical chemistry instruments measuring the same analyte. Clin Chem Lab Med 2023;62:853–60. https://doi.org/10.1515/cclm-2023-0965.Search in Google Scholar PubMed
23. Badrick, T, Fortun, M, Vayanos, Z, Bernard, M, Dufour, P, Souied, L, et al.. Quality control for serological testing. Clin Chim Acta 2024;564:119905. https://doi.org/10.1016/j.cca.2024.119905.Search in Google Scholar PubMed
24. Quinton, M, Newton, DW, Neil, B, Mitchell, S, Mostafa, HH. Quality control data management with unity real-time in molecular virology. J Clin Virol 2024;171. https://doi.org/10.1016/j.jcv.2024.105655.Search in Google Scholar PubMed
25. Farrell, CJL, Carter, AC. Serum indices: managing assay interference. Ann Clin Biochem 2016;53:527–38. https://doi.org/10.1177/0004563216643557.Search in Google Scholar PubMed
26. Hardie, RA, Moore, D, Holzhauser, D, Legg, M, Georgiou, A, Badrick, T. Informatics external quality assurance (IEQA) Down under: evaluation of a pilot implementation. LaboratoriumsMedizin 2018;42:297–304. https://doi.org/10.1515/labmed-2018-0050.Search in Google Scholar
27. Koetsier, S, Jones, GRD, Badrick, T. Safe reading of chemical pathology reports: the RCPAQAP report assessment survey. Pathology 2016;48:357–62. https://doi.org/10.1016/j.pathol.2016.02.018.Search in Google Scholar PubMed
28. Badrick, T, Tseung, J, Frogley, M, Chai, SY, Lidbury, BA. Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting. Clin Chim Acta 2025;572:120263. https://doi.org/10.1016/j.cca.2025.120263.Search in Google Scholar PubMed
29. Braga, F, Pasqualetti, S, Panteghini, M. The role of external quality assessment in the verification of in vitro medical diagnostics in the traceability era. Clin Biochem 2018;57:23–8. https://doi.org/10.1016/j.clinbiochem.2018.02.004.Search in Google Scholar PubMed
30. Farrell, CJL, Jones, GRD, Sikaris, KA, Badrick, T, Graham, P, Bush, J. Sharing reference intervals and monitoring patients across laboratories – findings from a likely commutable external quality assurance program. Clin Chem Lab Med 2024;62:2037–47. https://doi.org/10.1515/cclm-2024-0041.Search in Google Scholar PubMed
31. Panteghini, M. An improved implementation of metrological traceability concepts is needed to benefit from standardization of laboratory results. Clin Chem Lab Med 2024;63:270–8. https://doi.org/10.1515/cclm-2024-0428.Search in Google Scholar PubMed
32. Barton, R, Mackay, M, Jones, GR, Badrick, T. The management of post analytical correction factors. Clin Biochem Rev 2017;38:101–3.Search in Google Scholar
33. Badrick, T, Graham, P, Soufi, J, Holzhauser, D. Standardising EQA submissions with automated defactoring of post-analytical correction factors. Clin Chim Acta 2025;577:120468. https://doi.org/10.1016/j.cca.2025.120468.Search in Google Scholar PubMed
34. Steindel, SJ, Tetrault, G. Quality control practices for calcium, cholesterol, digoxin, and hemoglobin. Arch Pathol Lab Med 1998;122:401–8.Search in Google Scholar
35. Badrick, T, Brown, AS. Identifying human factors as a source of error in laboratory quality control. J Lab Precis Med 2023;8:1–6. https://doi.org/10.21037/jlpm-23-7.Search in Google Scholar
36. Brown, AS, Badrick, T. The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance. Clin Chem Lab Med 2023;61:37–43. https://doi.org/10.1515/cclm-2022-0409.Search in Google Scholar PubMed
37. Kristensen, GBB, Aakre, KM, Kristoffersen, AH, Sandberg, S. How to conduct external quality assessment schemes for the pre-analytical phase? Biochem Med (Zagreb) 2014;24:114–22. https://doi.org/10.11613/bm.2014.013.Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
